Noise may be added to a digital picture or a video sequence of digital pictures from various sources in different processing stages. For example, noise may be introduced during the acquisition process because of imperfections of capturing devices and poor lightning conditions. Noise can cause not only visual degradation of video sequences of digital pictures but also the reduction of coding efficiency in encoding process of the video sequences of digital pictures. The noise reduction process can be highly computational in some methods, and integrating a complex noise reduction method into a software based real-time video encoder may not be practical. There is also a need to reduce noise without generating any visual artifacts.
Various noise filtering techniques have been developed. Such noise filtering techniques include non-motion compensated type filtering and motion compensated type filtering.
Non-motion compensated technique does not require computationally extensive motion compensation process, and it usually assumes some kind of stationary, spatial or temporal information, or both, in sequences, or include motion information by adapting weighting of filtering coefficients. Non-motion compensated noise reduction methods generally do not make use of the advantage of temporal filtering.
On the other hand, the motion compensated algorithms exploit temporal correlation between video frames (digital pictures) in filtering, where temporal filtering is performed on motion compensated pixels. However, simply applying motion compensation to whole frames (digital pictures) may cause artifacts in parts of the video scene, especially stationary scene, due to inconsistencies in motion information obtained.
To counter this problem of motion compensated algorithms, adaptive motion compensated filtering is performed by detecting the difference between same position blocks of frames (digital pictures) of the video scene. Segmentation of object and background on a digital picture is applied before motion compensation is applied to object, and temporal filtering is applied to the background thereafter.
However, the motion compensated techniques generally need computationally expensive motion information finding process prior to filtering. Further, temporal filtering for homogeneous region cannot reduce noise effectively. Another problem in existing noise reduction methods is the lost of details for edge and textural region of the picture.